Garuda - Garba Rujukan Digital

The Impact of War on The Cryptocurrency Economy from a Management Perspective Suryari Purnama; Bayu Laksma Pradana; Gautam Khanna; Suhandi Suhandi; Agung Rizky; Ihsan Nuril Hikam; Muhammad Farhan Kamil
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.169

Armed conflicts and wars are increasingly shaping the global economic landscape, impacting both traditional markets and the burgeoning cryptocurrency economy. Cryptocurrencies, underpinned by blockchain technology, hold revolutionary potential for transactions, investments, and trading. However, their decentralized and global nature leaves them vulnerable to external shifts, particularly geopolitical events like war. This research explores the influence of war on cryptocurrency from a management perspective, analyzing how conflict impacts regulation, investment patterns, and technology adoption within the cryptocurrency ecosystem. By employing a literature based approach, this study aims to elucidate how global political and security shifts affect the cryptocurrency market. The findings indicate high reliability in the observed variables Investor, Crypto Market, and War with Cronbach alpha values ranging from 0.832 to 0.878, and rhoA values between 0.860 and 0.881. Additionally, composite reliability scores are robust, ranging from 0.860 to 0.882, demonstrating strong measurement reliability. The Average Variance Extracted (AVE) values, between 0.603 and 0.673, confirm that these measurement variables significantly explain the variance of the latent constructs. These results underscore the efficacy of the developed model in analyzing the interplay between war and cryptocurrency markets, contributing valuable insights into the sector resilience and adaptability amid geopolitical conflicts.

The Impact of War on The Cryptocurrency Economy from a Management Perspective Suryari Purnama; Bayu Laksma Pradana; Gautam Khanna; Suhandi Suhandi; Agung Rizky; Ihsan Nuril Hikam; Muhammad Farhan Kamil
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.169

Armed conflicts and wars are increasingly shaping the global economic landscape, impacting both traditional markets and the burgeoning cryptocurrency economy. Cryptocurrencies, underpinned by blockchain technology, hold revolutionary potential for transactions, investments, and trading. However, their decentralized and global nature leaves them vulnerable to external shifts, particularly geopolitical events like war. This research explores the influence of war on cryptocurrency from a management perspective, analyzing how conflict impacts regulation, investment patterns, and technology adoption within the cryptocurrency ecosystem. By employing a literature based approach, this study aims to elucidate how global political and security shifts affect the cryptocurrency market. The findings indicate high reliability in the observed variables Investor, Crypto Market, and War with Cronbach alpha values ranging from 0.832 to 0.878, and rhoA values between 0.860 and 0.881. Additionally, composite reliability scores are robust, ranging from 0.860 to 0.882, demonstrating strong measurement reliability. The Average Variance Extracted (AVE) values, between 0.603 and 0.673, confirm that these measurement variables significantly explain the variance of the latent constructs. These results underscore the efficacy of the developed model in analyzing the interplay between war and cryptocurrency markets, contributing valuable insights into the sector resilience and adaptability amid geopolitical conflicts.

The Impact of Digital Content Marketing Strategies on Perceived Usefulness and VALORANT Game Acceptance Nengah Sukendri; Zulfadli Ardiansyah; Agung Rizky; Asri Asri; Henry Zainarthur
ADI Bisnis Digital Interdisiplin Jurnal Vol 6 No 2 (2025): ADI Bisnis Digital Interdisiplin (ABDI Jurnal)
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/abdi.v6i2.1335

In the digital gaming industry that is becoming increasingly competitive, content marketing strategies play a crucial role in shaping players’ perceptions of a game’s quality and usefulness while influencing their decision to adopt it. As one of Riot games’ most prominent titles, VALORANT faces market dynamics that require digital marketing strategies that are informative, engaging, and sustainable. This study aims to analyze the influence of digital content marketing strategies on perceived usefulness and game acceptance using the Technology Acceptance Model (TAM) framework. The research employs a quantitative approach through an online survey of active VALORANT players, and the data are analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine causal relationships within TAM. The findings reveal that digital content marketing strategies have a significant positive effect on perceived usefulness, while Perceived Ease of Use (PEOU) also enhances Perceived Usefulness (PU) and overall game acceptance. Relevant and consistent content strengthens players’ perceived value, whereas ease of use supports a more comfortable and enjoyable gaming experience. This study highlights that strong digital content marketing strategies combined with a user-friendly game design are key factors in increasing player acceptance and engagement. The findings offer essential insights for developers and marketers in formulating effective content strategies to expand adoption, boost retention, and sustain the game’s community ecosystem.

Optimization of Machine Learning Algorithms for Fraud Detection in E-Payment Systems Agung Rizky; Ahmad Gunawan; Maulana Arif Komara; Muchlisina Madani; Ethan Harris
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.68

This study explores the optimization of machine learning algorithms for fraud detection in electronic payment (e-payment) systems. The rapid growth of e-payment platforms has introduced significant challenges in ensuring the security and integrity of financial transactions. Fraud detection plays a pivotal role in mitigating these risks, and the application of machine learning (ML) has emerged as a powerful tool to identify fraudulent activities. This research examines how Data Quality (DQ), Algorithm Selection (AS), and Optimization Techniques (OT) influence Model Performance (MP) and, subsequently, Fraud Detection Effectiveness (FDE). The study utilizes Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 3 to analyze the relationships between these variables. The results demonstrate that high Data Quality significantly enhances Model Performance, while Algorithm Selection and Optimization Techniques also contribute positively, albeit to a lesser extent. The findings reveal that Model Performance plays a crucial mediating role between these factors and the effectiveness of fraud detection. Fraud Detection Effectiveness is found to be significantly impacted by Model Performance, suggesting that improving model accuracy and efficiency is essential for better fraud detection outcomes. Reliability and validity tests show strong internal consistency for all constructs, with Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE) all reaching satisfactory levels. The study highlights the importance of data preprocessing, the careful selection of machine learning models, and optimization techniques in achieving high-performing fraud detection systems. The results provide valuable insights for the development of more robust and scalable fraud detection mechanisms in e-payment systems, contributing to the broader field of machine learning and cybersecurity. Future research could explore advanced techniques like deep learning and blockchain integration for further enhancement of fraud detection systems.

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